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Kaggle MCP server

by Seif-Sameh·io.github.Seif-Sameh/Kaggle-mcp·v0.1.1
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MCP server for the Kaggle API: competitions, datasets, kernels, and models.

B72/100grade B
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B72/100

full report

Adoption
New

0 stars28 downloads/wk

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Kaggle tools (39, 11 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • competition_download_file

    Download a specific file from a Kaggle competition.

  • competition_download_files

    Download all files from a Kaggle competition.

  • competition_leaderboard_download

    Download the leaderboard data for a Kaggle competition to a local file.

  • competition_leaderboard_view

    Retrieve the current leaderboard for a Kaggle competition.

  • competition_list_files

    List files available in a Kaggle competition.

  • competition_submissions

    Get a list of all the submissions for a particular competition.

  • competition_submitwrite action

    Submit a file to a Kaggle competition.

  • competitions_list

    List Kaggle competitions with optional filtering.

  • dataset_createwrite action

    Create a new Kaggle dataset from a local folder with metadata configuration.

  • dataset_create_versionwrite action

    Create a new version of an existing Kaggle dataset.

  • dataset_download_file

    Download a specific file from a Kaggle dataset to a local directory.

  • dataset_download_files

    Download all files from a Kaggle dataset to a local directory.

  • dataset_initialize

    Initialize a new Kaggle dataset in a local folder with metadata configuration.

  • dataset_list_files

    Retrieve a list of all files contained within a specific Kaggle dataset.

  • dataset_metadata

    Download the metadata file for a dataset.

  • dataset_status

    Get the status of a dataset. Only for your own datasets.

  • datasets_list

    Return a list of datasets.

  • kernel_initialize

    Initialize kernel metadata configuration in a specified folder.

  • kernel_list_files

    List files associated with a specific Kaggle kernel.

  • kernel_output

    Download the execution output files from a Kaggle kernel.

  • kernel_pull

    Download a Kaggle kernel and its files to a local directory.

  • kernel_push

    Push a Kaggle kernel and its metadata from a local folder.

  • kernel_status

    Get the status of a Kaggle kernel.

  • kernels_list

    Search and list Kaggle kernels based on specified criteria.

  • model_createwrite action

    Create a new model on Kaggle using metadata from a local folder.

  • model_deletewrite action

    Delete a model from Kaggle.

  • model_get

    Retrieve metadata and details for a specific Kaggle model, and save it as a JSON file.

  • model_initialize

    Initialize model metadata configuration in a specified folder.

  • model_instance_createwrite action

    Create a new model instance on Kaggle using metadata from a local folder.

  • model_instance_deletewrite action

    Delete a model instance from Kaggle.

  • model_instance_get

    Retrieve details of a specific model instance from Kaggle and save as JSON.

  • model_instance_initialize

    Initialize model instance metadata configuration in a specified folder.

  • model_instance_updatewrite action

    Update an existing model instance using metadata from a local folder.

  • model_instance_version_createwrite action

    Create a new version of an existing model instance.

  • model_instance_version_deletewrite action

    Delete a specific version of a model instance from Kaggle.

  • model_instance_version_download

    Download all files for a model instance version to a local directory.

  • model_instance_version_files

    List all files contained in a specific model instance version.

  • model_updatewrite action

    Update an existing model on Kaggle using metadata from a local folder.

  • models_list

    Search and list Kaggle models based on specified criteria.

Public scan report

scanner v0.1.9 · 2026-09-27 · same rubric, same numbers if you re-run it

no findings
  • Code scan5 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitystatic API keys via environment variables6/15
  • Maintenancelast push 107 days ago8/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 72/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Kaggle compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
72/100
higher than 38% of 48,255 servers
Tools
39
the typical server has 7
Tools that change things
28%
typical: 0%. Fewer write tools means less an agent can break
Downloads a week
28
more than 15% of 12,191 servers with a package

What its tools let an agent do: 28 read · 11 change

Install Kaggle in Claude Code, Cursor or VS Code

claude mcp add Kaggle-mcp -- uvx mcp-server-kaggle
Add to Cursor

What the publisher says

From the Kaggle repository's README, as published. We do not edit it. Read it on GitHub

Kaggle MCP Server

<!-- mcp-name: io.github.Seif-Sameh/Kaggle-mcp -->

A Model Context Protocol (MCP) server that provides seamless integration with the Kaggle API. Interact with Kaggle competitions, datasets, kernels, and models through MCP-compatible clients like Claude Desktop.

Features

  • Competitions: List, download files, submit, view leaderboards and submissions
  • Datasets: Search, download, create, and manage datasets with version control
  • Kernels: List, push, pull, and manage Kaggle notebooks and scripts
  • Models: Create, update, and manage ML models and instances with full version control

Installation

Prerequisites

  • Python 3.10 or higher
  • A Kaggle account with API credentials

Install from PyPI

The recommended way is to run the server with uvx, which handles the install for you:

uvx mcp-server-kaggle

Or install it explicitly:

pip install mcp-server-kaggle
# or
uv tool install mcp-server-kaggle

Install from Source

For development or local modifications:

git clone https://github.com/Seif-Sameh/Kaggle-mcp.git
cd Kaggle-mcp
uv sync

Setup

1. Get Your Kaggle API Credentials

  1. Go to https://www.kaggle.com/account
  2. Scroll to the "API" section
  3. Click "Create New Token"
  4. This downloads kaggle.json with your credentials

2. Configure Credentials

Option A: Environment Variables (Recommended)

export KAGGLE_USERNAME=your_username
export KAGGLE_API_KEY=your_api_key

Or add to your ~/.zshrc or ~/.bashrc:

echo 'export KAGGLE_USERNAME=your_username' >> ~/.zshrc
echo 'export KAGGLE_API_KEY=your_api_key' >> ~/.zshrc
source ~/.zshrc

Option B: Using .env File

Create a .env file in your project directory:

KAGGLE_USERNAME=your_username
KAGGLE_API_KEY=your_api_key

Usage

With Claude Desktop

The recommended way to use Kaggle MCP is with Claude Desktop.

  1. Locate your Claude Desktop config file:
  • macOS: ~/Library/Application Support/Claude/claudedesktopconfig.json
  • Windows: %APPDATA%\Claude\claudedesktopconfig.json
  • Linux: ~/.config/Claude/claudedesktopconfig.json
  1. Add the Kaggle MCP server configuration:
{
  "mcpServers": {
    "kaggle": {
      "command": "uvx",
      "args": ["mcp-server-kaggle"],
      "env": {
        "KAGGLE_USERNAME": "YOUR_KAGGLE_USERNAME",
        "KAGGLE_API_KEY": "YOUR_KAGGLE_API_KEY"
      }
    }
  }
}

Running from a local source clone (alternative)

{
  "mcpServers": {
    "kaggle": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/Kaggle-mcp",
        "run",
        "mcp-server-kaggle"
      ],
      "env": {
        "KAGGLE_USERNAME": "YOUR_KAGGLE_USERNAME",
        "KAGGLE_API_KEY": "YOUR_KAGGLE_API_KEY"
      }
    }
  }
}
  1. Restart Claude Desktop
  1. Start using Kaggle through Claude!

Try asking Claude:

  • "List the latest Kaggle competitions"
  • "Download the Titanic dataset"
  • "Show me my recent competition submissions"
  • "Search for NLP datasets"

Standalone Usage

Run the MCP server directly:

mcp-server-kaggle

Or as a Python module:

python -m kaggle_mcp

Available Tools

Competitions (8 tools)

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Kaggle: common questions

Is Kaggle MCP server safe?
Mostly: it is graded B (72/100). Read the Kaggle safety report
How do I install Kaggle?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Kaggle need an API key?
Yes. The registry entry asks for KAGGLE_API_KEY.
Is Kaggle maintained?
The last commit was 145 days ago (2026-06-12). The latest release is v0.1.1.
How does Kaggle compare with other MCP servers?
safety score 72/100 (higher than 38% of 48,255 servers); tools 39 (the typical server has 7); tools that change things 28% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.